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Item does not contain fulltext, OBJECTIVE: Primary aldosteronism (PA) is one of the most frequent causes of secondary hypertension. Although clinical practice guidelines recommend a diagnostic process, details of the steps remain incompletely standardized. DESIGN: In the present SCOT-PA survey, we have investigated the diversity of approaches utilized for each diagnostic step in different expert centers through a survey using Google questionnaires. A total of 33 centers from 3 continents participated. RESULTS: We demonstrated a prominent diversity in the conditions of blood sampling, assay methods for aldosterone and renin, and the methods and diagnostic cutoff for screening and confirmatory tests. The most standard measures were modification of antihypertensive medication and sitting posture for blood sampling, measurement of plasma aldosterone concentration (PAC) and active renin concentration by chemiluminescence enzyme immunoassay, a combination of aldosterone-to-renin ratio with PAC as an index for screening, and saline infusion test in a seated position for confirmatory testing. The cutoff values for screening and confirmatory testing showed significant variation among centers. CONCLUSIONS: Diversity of the diagnostic steps may lead to an inconsistent diagnosis of PA among centers and limit comparison of evidence for PA between different centers. We expect the impact of this diversity to be most prominent in patients with mild PA. The survey raises 2 issues: the need for standardization of the diagnostic process and revisiting the concept of mild PA. Further standardization of the diagnostic process/criteria will improve the quality of evidence and management of patients with PA.

Item does not contain fulltext, CONTEXT: Sampling of blood in the supine position for diagnosis of pheochromocytoma and paraganglioma (PPGL) results in lower rates of false positives for plasma normetanephrine than seated sampling. It is unclear how inpatient vs outpatient testing and other preanalytical factors impact false positives. OBJECTIVE: We aimed to identify preanalytical precautions to minimize false-positive results for plasma metanephrines. METHODS: Impacts of different blood sampling conditions on plasma metanephrines were evaluated, including outpatient vs inpatient testing, sampling of blood in semi- vs fully recumbent positions, use of cannulae vs direct venipuncture, and differences in outside temperature. A total of 3147 patients at 10 tertiary referral centers were tested for PPGL, including 278 with and 2869 without tumors. Rates of false-positive results were analyzed. RESULTS: Outpatient rather than inpatient sampling resulted in 44% higher plasma concentrations and a 3.4-fold increase in false-positive results for normetanephrine. Low temperature, a semi-recumbent position, and direct venipuncture also resulted in significantly higher plasma concentrations and rates of false-positive results for plasma normetanephrine than alternative sampling conditions, although with less impact than outpatient sampling. Higher concentrations and rates of false-positive results for plasma normetanephrine with low compared with warm temperatures were only apparent for outpatient sampling. Preanalytical factors were without impact on plasma metanephrines in patients with PPGL. CONCLUSION: Although inpatient blood sampling is largely impractical for screening patients with suspected PPGL, other preanalytical precautions (eg, cannulae, warm testing conditions) may be useful. Inpatient sampling may be reserved for follow-up of patients with difficult to distinguish true- from false-positive results.

Item does not contain fulltext, BACKGROUND: Arterial hypertension is a major cardiovascular risk factor. Identification of secondary hypertension in its various forms is key to preventing and targeting treatment of cardiovascular complications. Simplified diagnostic tests are urgently required to distinguish primary and secondary hypertension to address the current underdiagnosis of the latter. METHODS: This study uses Machine Learning (ML) to classify subtypes of endocrine hypertension (EHT) in a large cohort of hypertensive patients using multidimensional omics analysis of plasma and urine samples. We measured 409 multi-omics (MOmics) features including plasma miRNAs (PmiRNA: 173), plasma catechol O-methylated metabolites (PMetas: 4), plasma steroids (PSteroids: 16), urinary steroid metabolites (USteroids: 27), and plasma small metabolites (PSmallMB: 189) in primary hypertension (PHT) patients, EHT patients with either primary aldosteronism (PA), pheochromocytoma/functional paraganglioma (PPGL) or Cushing syndrome (CS) and normotensive volunteers (NV). Biomarker discovery involved selection of disease combination, outlier handling, feature reduction, 8 ML classifiers, class balancing and consideration of different age- and sex-based scenarios. Classifications were evaluated using balanced accuracy, sensitivity, specificity, AUC, F1, and Kappa score. FINDINGS: Complete clinical and biological datasets were generated from 307 subjects (PA=113, PPGL=88, CS=41 and PHT=112). The random forest classifier provided ∼92% balanced accuracy (∼11% improvement on the best mono-omics classifier), with 96% specificity and 0.95 AUC to distinguish one of the four conditions in multi-class ALL-ALL comparisons (PPGL vs PA vs CS vs PHT) on an unseen test set, using 57 MOmics features. For discrimination of EHT (PA + PPGL + CS) vs PHT, the simple logistic classifier achieved 0.96 AUC with 90% sensitivity, and ∼86% specificity, using 37 MOmics features. One PmiRNA (hsa-miR-15a-5p) and two PSmallMB (C9 and PC ae C38:1

BACKGROUND: Arterial hypertension is a major cardiovascular risk factor. Identification of secondary hypertension in its various forms is key to preventing and targeting treatment of cardiovascular complications. Simplified diagnostic tests are urgently required to distinguish primary and secondary hypertension to address the current underdiagnosis of the latter. METHODS: This study uses Machine Learning (ML) to classify subtypes of endocrine hypertension (EHT) in a large cohort of hypertensive patients using multidimensional omics analysis of plasma and urine samples. We measured 409 multi-omics (MOmics) features including plasma miRNAs (PmiRNA: 173), plasma catechol O-methylated metabolites (PMetas: 4), plasma steroids (PSteroids: 16), urinary steroid metabolites (USteroids: 27), and plasma small metabolites (PSmallMB: 189) in primary hypertension (PHT) patients, EHT patients with either primary aldosteronism (PA), pheochromocytoma/functional paraganglioma (PPGL) or Cushing syndrome (CS) and normotensive volunteers (NV). Biomarker discovery involved selection of disease combination, outlier handling, feature reduction, 8 ML classifiers, class balancing and consideration of different age- and sex-based scenarios. Classifications were evaluated using balanced accuracy, sensitivity, specificity, AUC, F1, and Kappa score. FINDINGS: Complete clinical and biological datasets were generated from 307 subjects (PA=113, PPGL=88, CS=41 and PHT=112). The random forest classifier provided ∼92% balanced accuracy (∼11% improvement on the best mono-omics classifier), with 96% specificity and 0.95 AUC to distinguish one of the four conditions in multi-class ALL-ALL comparisons (PPGL vs PA vs CS vs PHT) on an unseen test set, using 57 MOmics features. For discrimination of EHT (PA + PPGL + CS) vs PHT, the simple logistic classifier achieved 0.96 AUC with 90% sensitivity, and ∼86% specificity, using 37 MOmics features. One PmiRNA (hsa-miR-15a-5p) and two PSmallMB (C9 and PC ae C38:1

Contains fulltext : 232525.pdf (Publisher’s version ) (Open Access), CONTEXT: Identification of patients with endocrine forms of hypertension (EHT) (primary hyperaldosteronism [PA], pheochromocytoma/paraganglioma [PPGL], and Cushing syndrome [CS]) provides the basis to implement individualized therapeutic strategies. Targeted metabolomics (TM) have revealed promising results in profiling cardiovascular diseases and endocrine conditions associated with hypertension. OBJECTIVE: Use TM to identify distinct metabolic patterns between primary hypertension (PHT) and EHT and test its discriminating ability. METHODS: Retrospective analyses of PHT and EHT patients from a European multicenter study (ENSAT-HT). TM was performed on stored blood samples using liquid chromatography mass spectrometry. To identify discriminating metabolites a "classical approach" (CA) (performing a series of univariate and multivariate analyses) and a "machine learning approach" (MLA) (using random forest) were used.The study included 282 adult patients (52% female; mean age 49 years) with proven PHT (n = 59) and EHT (n = 223 with 40 CS, 107 PA, and 76 PPGL), respectively. RESULTS: From 155 metabolites eligible for statistical analyses, 31 were identified discriminating between PHT and EHT using the CA and 27 using the MLA, of which 16 metabolites (C9, C16, C16:1, C18:1, C18:2, arginine, aspartate, glutamate, ornithine, spermidine, lysoPCaC16:0, lysoPCaC20:4, lysoPCaC24:0, PCaeC42:0, SM C18:1, SM C20:2) were found by both approaches. The receiver operating characteristic curve built on the top 15 metabolites from the CA provided an area under the curve (AUC) of 0.86, which was similar to the performance of the 15 metabolites from MLA (AUC 0.83). CONCLUSION: TM identifies distinct metabolic pattern between PHT and EHT providing promising discriminating performance.